Behaviour Prediction in a Learning Management System

Abstract

Learning Management Systems (LMS) lack automated intelligent components that analyse data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaire related to a specific learning style and cognitive psychometric tests have been used to identify such behaviour. The problem such method is that a leaner can give inaccurate information, time consuming and prone to errors. Although literature reports complex models predicting leaning styles, only a few have used machine learning methods such as k-nearest neighbour (KNN). The primary objective of this study was to design, develop and evaluate a model based on machine learning model for predicting LS from LMS log records. Approximately 200,000 log records of 199 students who had accessed e-Learning course for a 15-week semester were extracted from LMS to create a dataset. Machine learning concepts were identified from the log records. The dataset was split into training and testing set. A model using K-NN algorithm designed and implemented on using r-studio programming language. The model was trained to predict LS and classify each student based on FSLSM. From this, a model predicting learning behaviour based on the theory was developed and evaluated. Preliminary results are promising demonstrating the model after full validation can be relied on to identify the LS.